Statistical inference of travelers' route choice preferences with system-level data

Traditional network models encapsulate travel behavior among all origin-destination pairs based on a simplified and generic utility function. Typically, the utility function consists of travel time solely and its coefficients are equated to estimates obtained from stated preference data. While this modeling strategy is reasonable, the inherent sampling bias in individual-level data may be further amplified over network flow aggregation, leading to inaccurate flow estimates. This data must be collected from surveys or travel diaries, which may be labor intensive, costly and limited to a small time period. To address these limitations, this study extends classical bi-level formulations to estimate travelers' utility functions with multiple attributes using system-level data. We formulate a methodology grounded on non-linear least squares to statistically infer travelers' utility function in the network context using traffic counts, traffic speeds, traffic incidents and sociodemographic information, among other attributes. The analysis of the mathematical properties of the optimization problem and of its pseudo-convexity motivate the use of normalized gradient descent. We also develop a hypothesis test framework to examine statistical properties of the utility function coefficients and to perform attributes selection. Experiments on synthetic data show that the coefficients are consistently recovered and that hypothesis tests are a reliable statistic to identify which attributes are determinants of travelers' route choices. Besides, a series of Monte-Carlo experiments suggest that statistical inference is robust to noise in the Origin-Destination matrix and in the traffic counts, and to various levels of sensor coverage. The methodology is also deployed at a large scale using real-world multi-source data in Fresno, CA collected before and during the COVID-19 outbreak.